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Zitong Wang

6 accepted papers

2026

Design and Development of a Robot-Assisted In Situ Bioprinting System With Optical Tracking

RA-L 2026

Robot-assisted <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> bioprinting offers a superior workspace-to-occupied-space ratio and enables direct deposition of bioink onto damaged tissues, surpassing the capabilities of traditional bencht

Cited by 0SourceScholar
2026

DiffDecompose: Layer-Wise Decomposition of Alpha-Composited Images via Diffusion Transformers

CVPR 2026

Diffusion models have recently motivated great success in many generation tasks like object removal. Nevertheless, existing image decomposition methods struggle to disentangle semi-transparent or transparent layer occlusions due to mask prior dependencies, static object assumptions, and the lack of

Cited by 0SourcecodeScholar
2026

PonderLM-2: Pretraining LLM with Latent Thoughts in Continuous Space

ICML 2026spotlight

The remarkable success of Chain-of-Thought (CoT), which enhances performance by scaling generation steps at test-time, inspires us to ask: can we leverage a similar scaling of computational steps during pretraining to improve the generation of each individual token? To address this, we propose a nov…

Cited by 0SourceScholar
2025

Stochastic Trajectory Optimization for Robotic Skill Acquisition From a Suboptimal Demonstration

RA-L 2025

Learning from Demonstration (LfD) has emerged as a crucial method for robots to acquire new skills. However, when given suboptimal task trajectory demonstrations with shape characteristics reflecting human preferences but subpar dynamic attributes such as slow motion, robots not only need to mimic t

Cited by 0SourcecodeScholar
2024

A Robotic Solution to Peg in/out Hole Tasks with Latching Requirements

RA-L 2024

Connectors with latches, such as LC fiber connectors, RJ45 network cable connectors, and certain electronic connectors, have significant automation requirements for connection and disconnection. To accomplish not only the peg-in-hole but also the peg-out-hole tasks, careful consideration must be giv

Cited by 7SourceScholar
2024

Learning from Sparse Offline Datasets via Conservative Density Estimation

ICLR 2024poster

Offline reinforcement learning (RL) offers a promising direction for learning policies from pre-collected datasets without requiring further interactions with the environment. However, existing methods struggle to handle out-of-distribution (OOD) extrapolation errors, especially in sparse reward or…